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Bootstrapping Computer Vision Models With Synthetic Data
Training computer vision models is complex, iterative, and requires a vast amount of high-quality, relevant visual data. Traditionally, this process relies on visual data gathered from the real world...
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$500 USD
Duration
1 day
Course Code
NV-BCVMSD
Available Formats
Classroom
Overview
Training computer vision models is complex, iterative, and requires a vast amount of high-quality, relevant visual data. Traditionally, this process relies on visual data gathered from the real world with cameras and sensors, often manually labeled, to represent the scenarios and situations that the model needs to learn.
NVIDIA Omniverse™ Replicator is a powerful synthetic data generation (SDG) engine that produces physically simulated synthetic data for training deep neural networks (DNNs). It augments costly, laborious human-labeled data, which can be error-prone and incomplete, with diverse physically accurate data tailored to the needs of developers.
In this course, you’ll use Omniverse Replicator and the Omniverse Defects Generation Extension to generate synthetic data. Next, you’ll iterate on the dataset to train a DNN to find target objects (scratches) in a scene.
Skills Gained
- Create a synthetic training dataset for later processing using NVIDIA Omniverse Replicator
- Customize and refine existing tools to match your dataset feature and format requirements
- Parameterize data generation offline for faster iteration when creating new or refined datasets
- Import a synthetic dataset into your workflow, train it, iterate on the design, and export a model to be used for inference
Prerequisites
- Intermediate understanding of Python (including classes, objects, and decorators)
- Basic understanding of machine learning and deep learning concepts and pipelines
- Suggested materials to satisfy prerequisites: Python tutorial, Deep Learning in a Nutshell, Deep Learning Demystified
Course Details
Course Outline
Introduction
Introduction to Synthetic Data Generation (SDG) With Omniverse Replicator
- Learn how to create a synthetic training dataset for later processing.
- Discuss the case for synthetic data.
- Learn the basics of the Replicator Python API for SDG.
- Create example datasets using Python scripts using an NVIDIA Omniverse application interface.
- Create a defects dataset using the Omniverse Defects Generation Extension and the Omniverse Defects demo pack.
- Modify the extension code to change the dataset generated.
Headless SDG and Replicator YAML Extension
- Learn to parameterize data generation offline using the Replicator YAML extension for faster iteration when creating new or refined datasets:
- Discuss the advantages and disadvantages of running Omniverse Replicator in headless mode.
- Learn to run Omniverse Replicator in headless mode using a configuration file.
- Iterate on changes to the configuration file to generate new datasets.
Integrating Dataset Iteration Into the Training Workflow
- Learn how to import a synthetic dataset into your workflow, train it, iterate on the dataset design, and export a model to be used for inference:
- Discuss practical guidelines and examples for training a perception dataset to find a target object.
- Fine-tune a visual transformer (ViT) model using NVIDIA TAO as the example workflow.
- Iterate on the model by improving the data to solve accuracy issues.
- Export the model for later deployment.
Assessment and Q&A
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
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Reviews
They are very good and made sure we had all the appropriate materials for class.
Provided good amount of material and a great instructor to teach the material.
The training was good but needed the basic skills of maximo before getting deep in the configuration of it.
this class was informative, made me think about certifying for the suse manager cert.
Overall it was a good bootcamp. A lot to cover so it is understandable that the pace had to be a little fast.
